The interaction of image processing and computer vision technologies with imaging technologies has led to advancements in fields such as transportation monitoring, surveillance, and medical imaging, among others. In some applications, very high-resolution images are required to support algorithms for spatial feature extraction, tracking applications where object trajectories need to be known with certain accuracy, etc.
For example, in applications such as detecting the contour of a cancerous tumor in a human body, high-resolution thermal imaging is required. Silicon-based visible and near infrared (NIR) imaging sensors such as charge-coupled device (CCD) and complementary metal-oxide-semiconductor (CMOS) can be manufactured via common and inexpensive silicon processing techniques. Since silicon is photosensitive in the visible electromagnetic (EM) range, it is then possible to fabricate red-green-blue (RGB) and NIR sensors with resolutions of up to 10000 (H)×7096 (V) pixels on a 35 mm (diagonal length) chip relatively inexpensively. However, for thermal imaging (and other applications) the required pixel size is large in dimension by nature, and photo-sensitive material with sensitivity in those EM bands is not compatible with silicon manufacturing technologies. Thus, high-resolution imaging sensor chips sensitive in the thermal band are difficult and expensive to produce.
A need therefore exists for methods, systems, and apparatuses that enable high-definition imaging beyond the visible EM range by leveraging low-resolution sensor chips and compressive sensing concepts exploiting joint sparsity assumptions.